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import json
import numpy as np
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
import re
class SHLRecommender:
def __init__(self, data_path='shl_assessments_complete.json'):
with open(data_path) as f:
self.data = json.load(f)
self.model = SentenceTransformer('all-MiniLM-L6-v2')
self._prepare_data()
def _parse_duration(self, duration_str):
"""Parse duration string and return maximum minutes"""
if not duration_str or duration_str.lower() == 'not specified':
return float('inf') # Treat as no duration limit
# Extract numbers from duration string
numbers = re.findall(r'\d+', duration_str)
if not numbers:
return float('inf')
# Return the highest number in case of ranges
return max(map(int, numbers))
def _prepare_data(self):
"""Prepare assessment data for recommendation"""
self.assessments = self.data['assessments']
# Create text for embedding
self.texts = []
for assessment in self.assessments:
text = f"{assessment['name']} {assessment['description']} "
text += f"Skills: {', '.join(assessment.get('skills_tested', []))} "
text += f"Use cases: {', '.join(assessment.get('use_cases', []))}"
self.texts.append(text)
# Generate embeddings
self.embeddings = self.model.encode(self.texts)
def recommend(self, query, top_k=5, category=None, duration_max=None):
"""Get recommendations based on query and filters"""
query_embedding = self.model.encode([query])
similarities = cosine_similarity(query_embedding, self.embeddings)[0]
results = []
for idx, score in enumerate(similarities):
assessment = self.assessments[idx]
# Apply filters
if category and assessment['category'] != category:
continue
if duration_max is not None:
duration = self._parse_duration(assessment['duration'])
if duration > duration_max:
continue
results.append({
**assessment,
'score': float(score)
})
# Sort by similarity score
results.sort(key=lambda x: x['score'], reverse=True)
return results[:top_k]
def get_categories(self):
"""Get list of available categories"""
return self.data['metadata']['categories'] |